Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression. (May 2017) - Liu, Yiqi; Pan, Yongping; Huang, Daoping; Wang, Qilin
Abstract: The activated sludge process (ASP) is widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, the occurrence of filamentous sludge bulking often compromises the stable operation of the ASP. For timely diagnosis of filamentous sludge bulking for an activated sludge process in advance, this study proposed a Multi-Output Gaussian Processes Regression (MGPR) model for multi-step prediction and presented the Vector auto-regression (VAR) to learn the MGPR modelling deviation. The resulting models and associated uncertainty levels are used to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), such that the evolution of SVI can be predicted for both one-step and multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prognosis of filamentous bulking sludge with real-time SVI prediction were tested through a simulation study. The results demonstrated that the proposed methodology was capable of predicting future SVI with good accuracy, thereby providing sufficient time for filamentous sludge bulking. Highlights: A Multi-output Gaussian Processes Regression (MGPR) model is proposed for Multi-step prediction. Recursive and direct strategies are proposed to enhance predicted ability of MGPR model. The uncertainty information generated from MGPR is improved by the square root of the sum of the squares. The VAR model is proposed to correct the errors from uncertainty. Multi-step prediction with uncertainty information can support prognosis of filamentous bulking sludge efficiently.